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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Top 30 AWS Interview Questions with Answers

Abhishek Kumar
Abhishek Kumar Last Updated : 01 Aug, 2024
9 min read

Introduction

Are you preparing for an Amazon Web Services (AWS) interview? Whether you’re just starting or aiming for a senior position, this comprehensive list of questions will prepare you well for your next interview. The list includes the answers to the 30 most frequently asked AWS interview questions, that will help you get ready for any interview. We’ve divided the questions into three categories – beginner, associate, and professional – to help you prepare for AWS-related job roles at every level.

Also Read: AWS Instances | Machine Learning With AWS

Amazon Web Services

AWS Interview Questions & Answers – Beginner Level

Q1. What is AWS, and what are its main services?

Answer: AWS (Amazon Web Services) is a cloud computing platform provided by Amazon. It offers many services including computing power, database storage, content delivery, and other functionality to help businesses scale and grow. Some main services include:

  • EC2 (Elastic Compute Cloud) for virtual servers
  • S3 (Simple Storage Service) for scalable storage
  • RDS (Relational Database Service) for managed databases
  • Lambda for serverless computing
  • CloudFront for content delivery

Q2. Explain the difference between EC2 and S3.

Answer: EC2 (Elastic Compute Cloud) provides resizable computing capacity in the cloud. It’s essentially virtual servers for running applications. S3 (Simple Storage Service), on the other hand, is an object storage service offering industry-leading scalability, data availability, security, and performance. EC2 is for computation, while S3 is for storage.

Q3. What is an AMI in AWS?

Answer: AMI stands for Amazon Machine Image. It’s a pre-configured package required to launch an EC2 instance. It includes the operating system, application server, and applications. AMIs can be customized, saved, and reused, making it easier to create and manage instances.

Q4. What is the AWS Free Tier?

Answer: The AWS Free Tier provides customers the ability to explore and try out AWS services free of charge up to specified limits for each service. It’s available to new AWS customers for 12 months following their AWS sign-up date. It includes services such as EC2, S3, and RDS, among others.

Q5. Describe the concept of Availability Zones in AWS.

Answer: Availability Zones are distinct locations within an AWS Region that are engineered to be isolated from failures in other Availability Zones. They provide inexpensive, low-latency network connectivity to other Availability Zones in the same AWS Region. This design allows for the creation of highly available and fault-tolerant applications.

Q6. What is AWS CloudFront?

Answer: AWS CloudFront is a fast content delivery network (CDN) service that securely delivers data, videos, applications, and APIs to customers globally with low latency and high transfer speeds. It integrates with other AWS services to provide an easy way to distribute content to end users with low latency and high data transfer speeds.

Q7. Explain the purpose of AWS IAM.

Answer: IAM (Identity and Access Management) is a web service that helps you securely control access to AWS resources. It allows you to manage users, groups, and permissions that control which AWS resources users can access. IAM is used to create and manage AWS users and groups and use permissions to allow and deny their access to AWS resources.

Q8. What is Amazon RDS?

Answer: Amazon RDS (Relational Database Service) is a managed database service that makes it easy to set up, operate, and scale a relational database in the cloud. It provides cost-efficient and resizable capacity while managing time-consuming database administration tasks, allowing you to focus on your applications and business.

Q9. Describe the difference between stopping and terminating an EC2 instance.

Answer: Stopping an EC2 instance means shutting down the instance, but you can start it again later. All EBS volumes remain attached, and you can start the instance again at any time. When you terminate an instance, it means you’re permanently deleting it. Any attached EBS volumes are detached and deleted (unless the delete-on-termination flag is set to false). You cannot restart a terminated instance.

Q10. What is AWS Lambda, and how does it work?

Answer: AWS Lambda is a serverless compute service that runs your code in response to events and automatically manages the underlying compute resources for you. You can use Lambda to run code for virtually any type of application or backend service – all with zero administration. You upload your code and Lambda takes care of everything required to run and scale your code with high availability.

AWS Interview Questions & Answers – Associate Level

Q11. Explain the differences between public, private, and hybrid cloud architectures in AWS.

Answer:

  • Public Cloud: All resources are hosted on AWS’s infrastructure. It’s fully managed by AWS and accessible over the internet.
  • Private Cloud: Resources are used exclusively by one business or organization, either on-premises or hosted by a third-party service provider. AWS offers Amazon VPC for creating private networks.
  • Hybrid Cloud: A mix of public and private cloud, allowing data and applications to be shared between them. AWS provides services like Direct Connect and VPN to facilitate hybrid architectures.

Q12. What is Auto Scaling in AWS, and how does it work?

Answer: Auto Scaling helps you ensure that you have the correct number of Amazon EC2 instances available to handle the load for your application. You create collections of EC2 instances, called Auto Scaling groups.

If you specify the minimum number of instances in each Auto Scaling group, Auto Scaling ensures that your group never goes below this size. You can also specify the maximum number of instances in each Auto Scaling group, and Auto Scaling ensures that your group never goes above this size.

Q13. Describe the purpose and use cases of Amazon VPC.

Answer: Amazon Virtual Private Cloud (Amazon VPC) lets you provision a logically isolated section of the AWS Cloud where you can launch AWS resources in a virtual network that you define. You have complete control over your virtual networking environment, including selection of your own IP address range, creation of subnets, and configuration of route tables and network gateways. Use cases include hosting multi-tier web applications, creating a hybrid environment, and disaster recovery.

Q14. What is the difference between Amazon S3 storage classes?

Answer: Amazon S3 offers different storage classes designed for different use cases:

  • Standard: For frequently accessed data
  • Intelligent-Tiering: For data with unknown or changing access patterns
  • Standard-IA (Infrequent Access): For long-lived, but less frequently accessed data
  • One Zone-IA: Like Standard-IA, but data is stored in a single AZ
  • Glacier: For long-term archive and digital preservation
  • Glacier Deep Archive: For long-term archive and digital preservation with retrieval times of 12 to 48 hours

Q15. Explain the concept of Elastic IP in AWS.

Answer: An Elastic IP address is a static IPv4 address designed for dynamic cloud computing. With an Elastic IP address, you can mask the failure of an instance or software by rapidly remapping the address to another instance in your account. It remains allocated to your AWS account until you choose to release it.

Q16. What is AWS CloudFormation, and how is it used?

Answer: AWS CloudFormation is a service that helps you model and set up your AWS resources so you can spend less time managing those resources and more time focusing on your applications that run in AWS. You create a template that describes all the AWS resources that you want (like EC2 instances or RDS DB instances), and CloudFormation takes care of provisioning and configuring those resources for you.

Q17. Describe the purpose of Amazon CloudWatch.

Answer: Amazon CloudWatch is a monitoring and observability service built for DevOps engineers, developers, site reliability engineers (SREs), and IT managers. CloudWatch provides data and actionable insights to monitor your applications, respond to system-wide performance changes, optimize resource utilization, and get a unified view of operational health.

Q18. What is the difference between horizontal and vertical scaling in AWS?

Answer:
Horizontal Scaling (Scaling Out): This involves adding more instances to your application. In AWS, this is typically done using Auto Scaling groups.
Vertical Scaling (Scaling Up): This involves increasing the power of an existing instance. In AWS, this means stopping your instance and resizing it to an instance type with more RAM, CPU, etc.

Q19. Explain the concept of AWS Elastic Beanstalk.

Answer: AWS Elastic Beanstalk is an easy-to-use service for deploying and scaling web applications and services developed with Java, .NET, PHP, Node.js, Python, Ruby, Go, and Docker on familiar servers such as Apache, Nginx, Passenger, and IIS. You can simply upload your code and Elastic Beanstalk automatically handles the deployment, from capacity provisioning, load balancing, auto-scaling to application health monitoring.

Q20. What are AWS security groups, and how do they differ from Network ACLs? 

Answer: Security Groups and Network ACLs are both important tools for network security in AWS, but they work differently:
Security Groups act as a firewall for associated Amazon EC2 instances, controlling both inbound and outbound traffic at the instance level. They are stateful, meaning return traffic is automatically allowed.
Network ACLs act as a firewall for associated subnets, controlling both inbound and outbound traffic at the subnet level. They are stateless, meaning return traffic must be explicitly allowed by rules.

AWS Interview Questions & Answers – Professional Level

Q21. Describe the AWS Well-Architected Framework and its five pillars.

Answer: The AWS Well-Architected Framework is a guide for designing and operating reliable, secure, efficient, and cost-effective systems in the cloud. It’s based on five pillars:

  1. Operational Excellence: Running and monitoring systems to deliver business value
  2. Security: Protecting information and systems
  3. Reliability: Ensuring a workload performs its intended function correctly and consistently
  4. Performance Efficiency: Using computing resources efficiently
  5. Cost Optimization: Avoiding unnecessary costs

Q22. Explain the concept of AWS Direct Connect and its benefits.

Answer: AWS Direct Connect is a cloud service solution that makes it easy to establish a dedicated network connection from your premises to AWS. Using AWS Direct Connect, you can establish private connectivity between AWS and your data center, office, or colocation environment. Benefits include:

  • Reduced network costs
  • Increased bandwidth throughput
  • More consistent network experience than Internet-based connections

Q23. What is AWS Snowball, and when would you use it? 

Answer: AWS Snowball is a petabyte-scale data transport solution that uses secure appliances to transfer large amounts of data into and out of AWS. You would use Snowball when you need to transfer terabytes to petabytes of data to AWS, when you’re limited by time, network costs, or security concerns. It’s particularly useful for data center migrations, disaster recovery, or simply moving large datasets.

Q24. Describe the differences between AWS SQS and SNS. 

Answer:
SQS (Simple Queue Service) is a fully managed message queuing service for decoupling and scaling microservices, distributed systems, and serverless applications. It’s a pull-based system.
SNS (Simple Notification Service) is a fully managed pub/sub messaging service for both application-to-application (A2A) and application-to-person (A2P) communication. It’s a push-based system.
The main difference is that SQS is used for message queuing, while SNS is used for sending notifications.

Q25. Explain the concept of AWS Transit Gateway and its use cases. 

Answer: AWS Transit Gateway is a service that enables customers to connect their Amazon Virtual Private Clouds (VPCs) and their on-premises networks to a single gateway. It acts as a hub that controls how traffic is routed among all the connected networks which act like spokes. Use cases include:

  • Simplifying network architecture
  • Enabling centralized routing and security
  • Facilitating multi-account and multi-VPC connectivity

Q26. What is AWS GuardDuty, and how does it enhance security? 

Answer: Amazon GuardDuty is a threat detection service that continuously monitors for malicious activity and unauthorized behavior to protect your AWS accounts and workloads. It uses machine learning, anomaly detection, and integrated threat intelligence to identify and prioritize potential threats. GuardDuty analyzes tens of billions of events across multiple AWS data sources, such as AWS CloudTrail, Amazon VPC Flow Logs, and DNS logs.

Q27. Describe the process of implementing a multi-region disaster recovery strategy in AWS.

Answer: Implementing a multi-region disaster recovery strategy in AWS typically involves:

  • Replicating data across regions using services like S3 Cross-Region Replication or DynamoDB Global Tables
  • Using Route 53 for DNS failover
  • Implementing ELB in multiple regions
  • Using Auto Scaling groups in each region
  • Leveraging CloudFormation for infrastructure as code across regions
  • Utilizing services like AWS Backup for consistent backups across regions

Q28. Explain the concept of AWS Organizations and how it helps in managing multiple accounts.

Answer: AWS Organizations is an account management service that enables you to consolidate multiple AWS accounts into an organization that you create and centrally manage. It provides:

  • Centralized management of all your AWS accounts
  • Consolidated billing for all member accounts
  • Hierarchical grouping of your accounts to meet your budgetary, security, or compliance needs
  • Policies to centralize control over the AWS services and API actions that each account can access
  • Tools to help automate account creation and management

Q29. What is AWS CloudTrail, and how does it differ from CloudWatch?

Answer: AWS CloudTrail is a service that enables governance, compliance, operational auditing, and risk auditing of your AWS account. It provides the event history of your AWS account activity, including actions taken through the AWS Management Console, AWS SDKs, command line tools, and other AWS services.

CloudWatch, on the other hand, is a monitoring and observability service. While CloudTrail focuses on auditing API usage and account activity, CloudWatch focuses on performance monitoring, resource utilization, and operational data in logs.

Q30. Describe the benefits and use cases of AWS Serverless Architecture.

Answer: AWS Serverless Architecture allows you to build and run applications and services without thinking about servers. Benefits include:

  • No server management
  • Flexible scaling
  • Pay for value
  • Automated high availability

Use cases include:

  • Web and mobile backends
  • IoT backends
  • Data processing
  • Chatbots
  • Alexa skills

AWS provides several fully managed services for serverless architecture, including Lambda, API Gateway, S3, DynamoDB, and more.

Conclusion

These AWS interview questions cover all aspects of the topic from the fundamentals, to advanced concepts, and professional use cases. Knowing these answers in your interview will prove that you have a solid understanding of Amazon Web Services. So read them thoroughly and go ace that interview! All the best!

Wish to build a career in AWS? Watch this video to get you started: DataHour: Jumpstart your Career with AWS AI/ML

amazon web servicesAWSAWS interview questionsBeginnercareerinterview questionsjob interviews
Abhishek Kumar
Abhishek Kumar

Hello, I'm Abhishek, a Data Engineer Trainee at Analytics Vidhya. I'm passionate about data engineering and video games I have experience in Apache Hadoop, AWS, and SQL,and I keep on exploring their intricacies and optimizing data workflows 

:)

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12 FAQs on AWS Asked in Interviews
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Want to learn Cloud Computing? Begin your Journey with AWS!
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